Overview

Dataset statistics

Number of variables15
Number of observations457
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.7 KiB
Average record size in memory120.3 B

Variable types

NUM14
CAT1

Warnings

TotalDistance is highly correlated with TotalSteps and 1 other fieldsHigh correlation
TotalSteps is highly correlated with TotalDistance and 1 other fieldsHigh correlation
TrackerDistance is highly correlated with TotalSteps and 1 other fieldsHigh correlation
TotalSteps has 61 (13.3%) zeros Zeros
TotalDistance has 63 (13.8%) zeros Zeros
TrackerDistance has 66 (14.4%) zeros Zeros
LoggedActivitiesDistance has 433 (94.7%) zeros Zeros
VeryActiveDistance has 245 (53.6%) zeros Zeros
ModeratelyActiveDistance has 228 (49.9%) zeros Zeros
LightActiveDistance has 74 (16.2%) zeros Zeros
SedentaryActiveDistance has 419 (91.7%) zeros Zeros
VeryActiveMinutes has 241 (52.7%) zeros Zeros
FairlyActiveMinutes has 227 (49.7%) zeros Zeros
LightlyActiveMinutes has 72 (15.8%) zeros Zeros
Calories has 5 (1.1%) zeros Zeros

Reproduction

Analysis started2020-09-11 12:11:27.331473
Analysis finished2020-09-11 12:12:01.047951
Duration33.72 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Id
Real number (ℝ≥0)

Distinct35
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4628594643
Minimum1503960366
Maximum8877689391
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-09-11T17:42:01.144692image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1503960366
5-th percentile1624580081
Q12347167796
median4057192912
Q36391747486
95-th percentile8792009665
Maximum8877689391
Range7373729025
Interquartile range (IQR)4044579690

Descriptive statistics

Standard deviation2293781430
Coefficient of variation (CV)0.4955675764
Kurtosis-1.039194515
Mean4628594643
Median Absolute Deviation (MAD)2030840877
Skewness0.3527246238
Sum2.115267752e+12
Variance5.261433247e+18
MonotocityIncreasing
2020-09-11T17:42:01.274348image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
4057192912327.0%
 
4020332650327.0%
 
1624580081194.2%
 
1503960366194.2%
 
4702921684153.3%
 
2347167796153.3%
 
4445114986153.3%
 
6962181067143.1%
 
8253242879122.6%
 
4558609924122.6%
 
Other values (25)27259.5%
 
ValueCountFrequency (%) 
1503960366194.2%
 
1624580081194.2%
 
1644430081102.2%
 
1844505072122.6%
 
1927972279122.6%
 
ValueCountFrequency (%) 
8877689391122.6%
 
8792009665122.6%
 
858381505981.8%
 
8378563200122.6%
 
8253242879122.6%
 

ActivityDate
Categorical

Distinct32
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
4/3/2016
35 
4/4/2016
35 
4/5/2016
35 
4/2/2016
35 
4/1/2016
34 
Other values (27)
283 
ValueCountFrequency (%) 
4/3/2016357.7%
 
4/4/2016357.7%
 
4/5/2016357.7%
 
4/2/2016357.7%
 
4/1/2016347.4%
 
4/7/2016337.2%
 
4/6/2016337.2%
 
4/8/2016337.2%
 
4/9/2016327.0%
 
4/10/2016296.3%
 
Other values (22)12326.9%
 
2020-09-11T17:42:01.419494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-11T17:42:01.541245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length8
Mean length8.332603939
Min length8

TotalSteps
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct389
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6546.562363
Minimum0
Maximum28497
Zeros61
Zeros (%)13.3%
Memory size3.6 KiB
2020-09-11T17:42:01.656445image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11988
median5986
Q310198
95-th percentile15605.6
Maximum28497
Range28497
Interquartile range (IQR)8210

Descriptive statistics

Standard deviation5398.493064
Coefficient of variation (CV)0.824630205
Kurtosis0.6648241126
Mean6546.562363
Median Absolute Deviation (MAD)4120
Skewness0.803413395
Sum2991779
Variance29143727.36
MonotocityNot monotonic
2020-09-11T17:42:01.792083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06113.3%
 
419520.4%
 
449920.4%
 
554320.4%
 
634420.4%
 
209820.4%
 
1240920.4%
 
820.4%
 
720.4%
 
683510.2%
 
Other values (379)37982.9%
 
ValueCountFrequency (%) 
06113.3%
 
410.2%
 
720.4%
 
820.4%
 
1410.2%
 
ValueCountFrequency (%) 
2849710.2%
 
2757210.2%
 
2570110.2%
 
2413610.2%
 
2301410.2%
 

TotalDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct334
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.663522972
Minimum0
Maximum27.53000069
Zeros63
Zeros (%)13.8%
Memory size3.6 KiB
2020-09-11T17:42:01.930712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.409999967
median4.090000153
Q37.159999847
95-th percentile11.23999977
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.749999881

Descriptive statistics

Standard deviation4.082072268
Coefficient of variation (CV)0.8753194296
Kurtosis3.448976394
Mean4.663522972
Median Absolute Deviation (MAD)2.889999866
Skewness1.321383526
Sum2131.229998
Variance16.663314
MonotocityNot monotonic
2020-09-11T17:42:02.064383image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06313.8%
 
0.00999999977661.3%
 
7.67000007630.7%
 
4.7199997930.7%
 
1.91999995720.4%
 
8.11999988620.4%
 
4.7800002120.4%
 
1.54999995220.4%
 
0.829999983320.4%
 
5.32999992420.4%
 
Other values (324)37081.0%
 
ValueCountFrequency (%) 
06313.8%
 
0.00999999977661.3%
 
0.0199999995520.4%
 
0.0299999993310.2%
 
0.109999999410.2%
 
ValueCountFrequency (%) 
27.5300006910.2%
 
23.3899993910.2%
 
20.9099998510.2%
 
20.3899993910.2%
 
20.1399993910.2%
 

TrackerDistance
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct336
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.609846824
Minimum0
Maximum27.53000069
Zeros66
Zeros (%)14.4%
Memory size3.6 KiB
2020-09-11T17:42:02.206004image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.279999971
median4.090000153
Q37.110000134
95-th percentile11.13599968
Maximum27.53000069
Range27.53000069
Interquartile range (IQR)5.830000162

Descriptive statistics

Standard deviation4.068539937
Coefficient of variation (CV)0.8825759494
Kurtosis3.576888152
Mean4.609846824
Median Absolute Deviation (MAD)2.920000076
Skewness1.339050019
Sum2106.699999
Variance16.55301722
MonotocityNot monotonic
2020-09-11T17:42:02.334729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06614.4%
 
0.00999999977661.3%
 
7.67000007630.7%
 
4.7199997930.7%
 
1.91999995720.4%
 
1.54999995220.4%
 
5.32999992420.4%
 
0.829999983320.4%
 
6.98000001920.4%
 
1.42999994820.4%
 
Other values (326)36780.3%
 
ValueCountFrequency (%) 
06614.4%
 
0.00999999977661.3%
 
0.0199999995520.4%
 
0.0299999993310.2%
 
0.109999999410.2%
 
ValueCountFrequency (%) 
27.5300006910.2%
 
23.3899993910.2%
 
20.9099998510.2%
 
20.3899993910.2%
 
20.1399993910.2%
 

LoggedActivitiesDistance
Real number (ℝ≥0)

ZEROS

Distinct20
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1794273741
Minimum0
Maximum6.72705698
Zeros433
Zeros (%)94.7%
Memory size3.6 KiB
2020-09-11T17:42:02.455342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.3665432006
Maximum6.72705698
Range6.72705698
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8492318298
Coefficient of variation (CV)4.733011526
Kurtosis27.13615724
Mean0.1794273741
Median Absolute Deviation (MAD)0
Skewness5.159788142
Sum81.99830997
Variance0.7211947008
MonotocityNot monotonic
2020-09-11T17:42:02.559093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
043394.7%
 
2.09214711240.9%
 
2.25308108330.7%
 
4.83638000510.2%
 
2.69645500210.2%
 
5.4568638810.2%
 
3.21868801110.2%
 
3.9727950110.2%
 
6.7270569810.2%
 
0.0558429993710.2%
 
Other values (10)102.2%
 
ValueCountFrequency (%) 
043394.7%
 
0.0558429993710.2%
 
1.60934400610.2%
 
1.92630195610.2%
 
2.02777290310.2%
 
ValueCountFrequency (%) 
6.7270569810.2%
 
5.4568638810.2%
 
5.18984985410.2%
 
4.90128278710.2%
 
4.87598991410.2%
 

VeryActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct170
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.180897153
Minimum0
Maximum21.92000008
Zeros245
Zeros (%)53.6%
Memory size3.6 KiB
2020-09-11T17:42:02.687721image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.309999943
95-th percentile5.760000038
Maximum21.92000008
Range21.92000008
Interquartile range (IQR)1.309999943

Descriptive statistics

Standard deviation2.487158568
Coefficient of variation (CV)2.106160186
Kurtosis18.7096635
Mean1.180897153
Median Absolute Deviation (MAD)0
Skewness3.73064421
Sum539.669999
Variance6.185957745
MonotocityNot monotonic
2020-09-11T17:42:02.825506image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024553.6%
 
0.070000000361.3%
 
0.2540.9%
 
0.230000004230.7%
 
0.530.7%
 
0.330000013130.7%
 
0.100000001520.4%
 
2.01999998120.4%
 
0.870000004820.4%
 
1.79999995220.4%
 
Other values (160)18540.5%
 
ValueCountFrequency (%) 
024553.6%
 
0.00999999977610.2%
 
0.0199999995510.2%
 
0.0399999991110.2%
 
0.0599999986620.4%
 
ValueCountFrequency (%) 
21.9200000810.2%
 
16.8199996910.2%
 
14.7200002710.2%
 
12.2200002710.2%
 
12.0600004210.2%
 

ModeratelyActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct140
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4786433252
Minimum0
Maximum6.400000095
Zeros228
Zeros (%)49.9%
Memory size3.6 KiB
2020-09-11T17:42:02.964134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01999999955
Q30.6700000167
95-th percentile2.089999914
Maximum6.400000095
Range6.400000095
Interquartile range (IQR)0.6700000167

Descriptive statistics

Standard deviation0.8309951707
Coefficient of variation (CV)1.736146995
Kurtosis11.87056988
Mean0.4786433252
Median Absolute Deviation (MAD)0.01999999955
Skewness2.971412313
Sum218.7399996
Variance0.6905529737
MonotocityNot monotonic
2020-09-11T17:42:03.102735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
022849.9%
 
0.2551.1%
 
0.180000007251.1%
 
0.330000013140.9%
 
0.529999971440.9%
 
0.230000004240.9%
 
0.159999996440.9%
 
0.259999990540.9%
 
0.370000004840.9%
 
0.219999998840.9%
 
Other values (130)19141.8%
 
ValueCountFrequency (%) 
022849.9%
 
0.0199999995510.2%
 
0.0399999991120.4%
 
0.0500000007530.7%
 
0.0599999986610.2%
 
ValueCountFrequency (%) 
6.40000009510.2%
 
5.48999977110.2%
 
4.48999977110.2%
 
4.44000005710.2%
 
3.72000002910.2%
 

LightActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct295
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.890196937
Minimum0
Maximum12.51000023
Zeros74
Zeros (%)16.2%
Memory size3.6 KiB
2020-09-11T17:42:03.456789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8700000048
median2.930000067
Q34.460000038
95-th percentile6.515999985
Maximum12.51000023
Range12.51000023
Interquartile range (IQR)3.590000033

Descriptive statistics

Standard deviation2.237523344
Coefficient of variation (CV)0.7741767752
Kurtosis0.2750757813
Mean2.890196937
Median Absolute Deviation (MAD)1.749999762
Skewness0.5215831453
Sum1320.82
Variance5.006510715
MonotocityNot monotonic
2020-09-11T17:42:03.604394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
07416.2%
 
0.00999999977671.5%
 
4.61000013440.9%
 
3.540.9%
 
3.5099999930.7%
 
3.91000008630.7%
 
5.26999998130.7%
 
4.94999980930.7%
 
3.21000003830.7%
 
1.62999999530.7%
 
Other values (285)35076.6%
 
ValueCountFrequency (%) 
07416.2%
 
0.00999999977671.5%
 
0.0199999995510.2%
 
0.0299999993310.2%
 
0.109999999410.2%
 
ValueCountFrequency (%) 
12.5100002310.2%
 
1210.2%
 
9.36999988610.2%
 
8.61999988610.2%
 
8.14999961910.2%
 

SedentaryActiveDistance
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001903719882
Minimum0
Maximum0.1000000015
Zeros419
Zeros (%)91.7%
Memory size3.6 KiB
2020-09-11T17:42:03.723108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.009999999776
Maximum0.1000000015
Range0.1000000015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0084868013
Coefficient of variation (CV)4.45800949
Kurtosis54.03881246
Mean0.001903719882
Median Absolute Deviation (MAD)0
Skewness6.566301549
Sum0.8699999861
Variance7.202579631e-05
MonotocityNot monotonic
2020-09-11T17:42:03.819849image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
041991.7%
 
0.009999999776224.8%
 
0.0299999993361.3%
 
0.0399999991140.9%
 
0.0199999995520.4%
 
0.0599999986620.4%
 
0.100000001510.2%
 
0.0500000007510.2%
 
ValueCountFrequency (%) 
041991.7%
 
0.009999999776224.8%
 
0.0199999995520.4%
 
0.0299999993361.3%
 
0.0399999991140.9%
 
ValueCountFrequency (%) 
0.100000001510.2%
 
0.0599999986620.4%
 
0.0500000007510.2%
 
0.0399999991140.9%
 
0.0299999993361.3%
 

VeryActiveMinutes
Real number (ℝ≥0)

ZEROS

Distinct85
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.62363239
Minimum0
Maximum202
Zeros241
Zeros (%)52.7%
Memory size3.6 KiB
2020-09-11T17:42:03.941552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile78.4
Maximum202
Range202
Interquartile range (IQR)25

Descriptive statistics

Standard deviation28.91970375
Coefficient of variation (CV)1.739674163
Kurtosis6.928585794
Mean16.62363239
Median Absolute Deviation (MAD)0
Skewness2.38473603
Sum7597
Variance836.3492648
MonotocityNot monotonic
2020-09-11T17:42:04.081429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
024152.7%
 
1112.4%
 
392.0%
 
481.8%
 
271.5%
 
771.5%
 
1571.5%
 
1871.5%
 
561.3%
 
2561.3%
 
Other values (75)14832.4%
 
ValueCountFrequency (%) 
024152.7%
 
1112.4%
 
271.5%
 
392.0%
 
481.8%
 
ValueCountFrequency (%) 
20210.2%
 
16510.2%
 
12810.2%
 
12410.2%
 
12310.2%
 

FairlyActiveMinutes
Real number (ℝ≥0)

ZEROS

Distinct62
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.07002188
Minimum0
Maximum660
Zeros227
Zeros (%)49.7%
Memory size3.6 KiB
2020-09-11T17:42:04.225458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q316
95-th percentile46.4
Maximum660
Range660
Interquartile range (IQR)16

Descriptive statistics

Standard deviation36.20863518
Coefficient of variation (CV)2.770357656
Kurtosis224.7286191
Mean13.07002188
Median Absolute Deviation (MAD)1
Skewness13.02940846
Sum5973
Variance1311.065262
MonotocityNot monotonic
2020-09-11T17:42:04.362207image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
022749.7%
 
6143.1%
 
16122.6%
 
8112.4%
 
7102.2%
 
1192.0%
 
1792.0%
 
1292.0%
 
981.8%
 
1581.8%
 
Other values (52)14030.6%
 
ValueCountFrequency (%) 
022749.7%
 
130.7%
 
230.7%
 
330.7%
 
471.5%
 
ValueCountFrequency (%) 
66010.2%
 
14110.2%
 
13310.2%
 
12010.2%
 
11410.2%
 

LightlyActiveMinutes
Real number (ℝ≥0)

ZEROS

Distinct251
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.0700219
Minimum0
Maximum720
Zeros72
Zeros (%)15.8%
Memory size3.6 KiB
2020-09-11T17:42:04.513803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164
median181
Q3257
95-th percentile348.6
Maximum720
Range720
Interquartile range (IQR)193

Descriptive statistics

Standard deviation122.2053721
Coefficient of variation (CV)0.7185591605
Kurtosis0.3822726388
Mean170.0700219
Median Absolute Deviation (MAD)90
Skewness0.3525141968
Sum77722
Variance14934.15298
MonotocityNot monotonic
2020-09-11T17:42:04.692324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
07215.8%
 
161.3%
 
24851.1%
 
20840.9%
 
21240.9%
 
23040.9%
 
27640.9%
 
19030.7%
 
27230.7%
 
26330.7%
 
Other values (241)34976.4%
 
ValueCountFrequency (%) 
07215.8%
 
161.3%
 
230.7%
 
310.2%
 
610.2%
 
ValueCountFrequency (%) 
72010.2%
 
63010.2%
 
58610.2%
 
50610.2%
 
49110.2%
 

SedentaryMinutes
Real number (ℝ≥0)

Distinct315
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995.2822757
Minimum32
Maximum1440
Zeros0
Zeros (%)0.0%
Memory size3.6 KiB
2020-09-11T17:42:04.851897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile465.4
Q1728
median1057
Q31285
95-th percentile1440
Maximum1440
Range1408
Interquartile range (IQR)557

Descriptive statistics

Standard deviation337.021404
Coefficient of variation (CV)0.3386189146
Kurtosis-0.6782227116
Mean995.2822757
Median Absolute Deviation (MAD)300
Skewness-0.3655631139
Sum454844
Variance113583.4267
MonotocityNot monotonic
2020-09-11T17:42:04.991524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14406313.8%
 
143940.9%
 
118140.9%
 
132830.7%
 
112530.7%
 
77030.7%
 
115030.7%
 
118530.7%
 
84230.7%
 
70030.7%
 
Other values (305)36579.9%
 
ValueCountFrequency (%) 
3210.2%
 
6110.2%
 
7510.2%
 
9910.2%
 
14610.2%
 
ValueCountFrequency (%) 
14406313.8%
 
143940.9%
 
143820.4%
 
143210.2%
 
142810.2%
 

Calories
Real number (ℝ≥0)

ZEROS

Distinct383
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2189.452954
Minimum0
Maximum4562
Zeros5
Zeros (%)1.1%
Memory size3.6 KiB
2020-09-11T17:42:05.136167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile790.8
Q11776
median2062
Q32667
95-th percentile3716
Maximum4562
Range4562
Interquartile range (IQR)891

Descriptive statistics

Standard deviation815.4845229
Coefficient of variation (CV)0.3724603999
Kurtosis0.5200100199
Mean2189.452954
Median Absolute Deviation (MAD)422
Skewness0.2363575209
Sum1000580
Variance665015.0071
MonotocityNot monotonic
2020-09-11T17:42:05.276327image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1776112.4%
 
187881.8%
 
206061.3%
 
051.1%
 
182051.1%
 
142951.1%
 
192051.1%
 
132440.9%
 
193530.7%
 
177730.7%
 
Other values (373)40288.0%
 
ValueCountFrequency (%) 
051.1%
 
5010.2%
 
18210.2%
 
25110.2%
 
39920.4%
 
ValueCountFrequency (%) 
456210.2%
 
452610.2%
 
443010.2%
 
428610.2%
 
423410.2%
 

Interactions

2020-09-11T17:41:36.778960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:36.927352image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.044985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.162208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.280919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.410572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.597045image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.717752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.837430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:37.958109image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.083415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.205089image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.330783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.455569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.569799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.685455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.799384image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:38.907346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.017760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.132674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.240668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.356328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.468719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.582767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.702475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.818165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:39.936821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.054533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.161754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.275452image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.384161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.489875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.597948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.713610image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:40.820353image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.013113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.126477image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.240410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.359783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.490113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.607799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.722523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.828237image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:41.950910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.064606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.175817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.284523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.398192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.505324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.620523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.734247image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.847915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:42.964634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.079565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.196906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.314493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.421544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.549397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.671103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.788587image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:43.910261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.036894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.149627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.272265image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.392942image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.515122image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.641812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.762491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:44.887159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.011554image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.226487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.345170image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.466844image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.572592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.676314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.787985image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.890738image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:45.999018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.105732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.215439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.331130image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.440319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.557542image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.671238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.774960image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:46.892646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.006343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.120010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.235760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.368565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.481235image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.601912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.718628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.835430image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:47.957321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.075246image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.206162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.328222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.439196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.557137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.676816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.792536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:48.908226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.028831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.144105image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.261702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.377146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.493214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.615858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.731550image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.851101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:49.971806image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.081485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.198174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.436535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.551229image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.664494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.782212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:50.893943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.009832image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.142478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.260162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.382834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.499522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.620199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.740341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.850733image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:51.976429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.098524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.218766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.345895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.474060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.596732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.720429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.845096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:52.970760image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.100413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.224648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.354298image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.482954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.601609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.719356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.836454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:53.956662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.071386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.191063image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.303766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.420450image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.541133image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.658813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.781485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:54.904141image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.028778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.149484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.261185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.389799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.510984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.628699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.749377image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.875010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:55.992696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:56.113400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:56.236073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:56.359309image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:56.487969image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:56.611698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:56.895639image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.022303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.137964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.259670image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.378350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.497005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.614718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.739385image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.855054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:57.975731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.097406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.219041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.347177image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.469357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.595051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.718718image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.833868image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:58.942449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.050364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.154929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.261047image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.373943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.486422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.613484image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.720267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.828973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:41:59.947132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:42:00.059858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:42:00.184525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:42:00.306200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-11T17:42:05.409997image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-11T17:42:05.664262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-11T17:42:05.911018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-11T17:42:06.194261image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-11T17:42:00.548593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-11T17:42:00.871386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
015039603663/25/2016110047.117.110.02.570.464.070.033122058041819
115039603663/26/20161760911.5511.550.06.920.733.910.089172745882154
215039603663/27/2016127368.538.530.04.660.163.710.05652686051944
315039603663/28/2016132318.938.930.03.190.794.950.0392022410801932
415039603663/29/2016120417.857.850.02.161.094.610.028282437631886
515039603663/30/2016109707.167.160.02.360.514.290.0301322311741820
615039603663/31/2016122567.867.860.02.290.495.040.033122398201889
715039603664/1/2016122627.877.870.03.320.833.640.047212008661868
815039603664/2/2016112487.257.250.03.000.453.740.040112446361843
915039603664/3/2016100166.376.370.00.911.284.180.015303146551850

Last rows

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
44788776893914/3/2016152608.1900008.1900000.01.800.755.570.001061725910583864
44888776893914/4/20162077918.41000018.4100000.011.730.656.000.00781620811383662
44988776893914/5/2016106958.1200008.1200000.00.770.187.090.0110324611812834
45088776893914/6/20162413620.91000020.9100000.012.220.548.080.00871631810194039
45188776893914/7/2016109108.4200008.4200000.02.960.395.030.00321121211852947
45288776893914/8/20162301420.38999920.3899990.011.100.638.620.0070293599824196
45388776893914/9/2016164708.0700008.0700000.00.000.028.020.0090928910523841
45488776893914/10/20162849727.53000127.5300010.021.921.124.460.001284621110554526
45588776893914/11/2016106228.0600008.0600000.01.470.156.370.0118722511902820
45688776893914/12/201623501.7800001.7800000.00.000.001.780.000058531938